Welcome to LtU ILI’s documentation!

Contents:

LtU-ILI

The Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline is an all-in-one framework for performing machine learning parameter inference in astrophysics and cosmology. Given labeled training data ${(x_i,\theta_i)}_{i=1}^N$ or a stochastic simulator $x(\theta)$, LtU-ILI is designed to automatically train state-of-the-art neural networks to learn the data-parameter relationship and produce robust, well-calibrated posterior inference.

The pipeline is quick and easy to set up; here’s an example of training a Masked Autoregressive Flow (MAF) network to predict a posterior over parameters $y$, given input data $x$:

...  # Imports

X, Y = load_data()                              # Load training data and parameters
loader = ili.data.NumpyLoader(X, Y)             # Create a data loader

trainer = ili.inference.InferenceRunner.load(
  backend = 'sbi', engine='NPE',                # Choose a backend and inference engine (here, Neural Posterior Estimation)
  prior = ili.utils.Uniform(low=-1, high=1),    # Define a prior 
  # Define a neural network architecture (here, MAF)
  nets = [ili.utils.load_nde_sbi(engine='NPE', model='maf')]  
)

posterior, _ = trainer(loader)                  # Run training to map data -> parameters

samples = posterior.sample(                     # Generate 1000 samples from the posterior for input x[0]
  x=X[0], sample_shape=(1000,)
)

Beyond this simple example, LtU-ILI comes with a wide range of customizable complexity, including:

Posterior-, Likelihood-, and Ratio-Estimation methods for ILI, including Sequential learning analogs
Various neural density estimators (Mixture Density Networks, Conditional Normalizing Flows, ResNet-like ratio classifiers)
Fully-customizable, exotic embedding networks (including CNNs and Graph Neural Networks)
A unified interface for multiple ILI backends (sbi, pydelfi, and lampe)
Multiple marginal and multivariate posterior coverage metrics
Jupyter and command-line interfaces
A parallelizable configuration framework for efficient hyperparameter tuning and production runs

For more details on the motivation, design, and theoretical background of this project, see the software release paper.

Getting Started

To install LtU-ILI, follow the instructions in INSTALL.md.

To get started, try out the tutorial for the Jupyter notebook interface in notebooks/ or the command line interface in examples/.

API Documentation

The documentation for this project can be found at this link.

References

We keep an updated repository of relevant interesting papers and resources at this link.

Contributing

Before contributing, please familiarize yourself with the contribution workflow described in CONTRIBUTING.md.

Recent Changes

We keep a record of recent changes in the project in the CHANGELOG.md file. If you are interested in the latest features, bug fixes, and improvements, you can find them there.

Contact

If you have comments, questions, or feedback, please write us an issue. The current leads of the Learning the Universe ILI working group are Benjamin Wandelt (benwandelt@gmail.com) and Matthew Ho (matthew.annam.ho@gmail.com).

Acknowledgements

This work is supported by the Simons Foundation through the Simons Collaboration on Learning the Universe.

_{Matt Ho} 💻 🎨 💡 📖 👀 🚇 🖋 🔬	_{Deaglan Bartlett} 💻 🎨 💡 📖 👀 🚇 🖋 🔬	_{Nicolas Chartier} 💡 📖 🔬 💻 🎨 👀 🖋	_{Carolina Cuesta} 💻 🎨 💡 📖 👀 🔬	_Simon 💻 💡	_{Axel Lapel} 💻 🔬 💡
_{Pablo Lemos} 🎨 💻	_{Chris Lovell} 🔬 💡 🔣 🖋	_{T. Lucas Makinen} 💻 🔬	_{Chirag Modi} 🎨 💻	_{Shivam Pandey} 🔬 💡	_{L.A. Perez} 🔬 🖋